{ "cells": [ { "cell_type": "markdown", "id": "96300fd7-2df1-48bf-bdef-d5377e1bd7a2", "metadata": {}, "source": [ "# Project" ] }, { "cell_type": "markdown", "id": "8797f24a-706d-45e6-ba24-7836d87bf1a9", "metadata": {}, "source": [ "## What Makes Us Happy? A Dive into the World Happiness Report 2024\n", "Hello readers! It’s finally a new year! We’ve counted down, cheered, and wished each other a heartfelt “Happy New Year!”. Because, let’s face it, deep down, we all want each other to be happy, right? Well, guess what? The World Happiness Report 2024 is here to tell us exactly what that means." ] }, { "cell_type": "markdown", "id": "4ae462df-4e4e-4c1c-ac86-996d467d0272", "metadata": {}, "source": [ "Happiness, it’s that thing we’re all chasing. Whether it’s through watching endless cat videos, making it onto the bus just in time despite being late, or managing to avoid spilling coffee all over yourself before a big meeting. But have you ever stopped to wonder, \"What actually makes us happy?\", Is it money?, Friends?, Freedom? (Or is it just making it through mondays without a meltdown?). Well, guess what?, The World Happiness Report 2024 is here to tell us exactly what that means!" ] }, { "cell_type": "markdown", "id": "0130c98e-d7d7-4d53-86d1-01ee5a8ab22e", "metadata": {}, "source": [ "The World Happiness Report 2024 has some answers, or at least a few clues. And let me tell you, this isn’t just another clickbait list like \"Top 10 Happiest Countries That’ll Make You Jealous.\" Oh no, this is some serious, serious business, brought to you by the brainiest folks from Gallup, the Oxford Wellbeing Research Centre, the UN Sustainable Development Solutions Network, and the WHR Editorial Board. (Try saying all that in one breath.)" ] }, { "cell_type": "markdown", "id": "b6d6aef0-92df-47de-a261-9094012b7b3b", "metadata": {}, "source": [ "Their mission? To convince governments and societies that happiness isn’t just some fluffy, optional feeling. It’s actually a pretty solid way to measure progress. You might then wonder \"How do they do it?\". They do it by asking alot of people a deceptively simple question:\n", "\n", "“Imagine your life as a ladder. The best possible life is a 10, and the worst is a 0. Where are you standing right now?”\n", "\n", "No but really the The Cantril Ladder asks respondents to think of a ladder, with the best possible life for them being a 10 and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale. It actually turns out that the answers to that question can tell us a lot. Using these responses, they identified six magical ingredients to happiness. And they are the following:\n", "\n", "1. GDP per capita (because let’s be honest, life’s a little easier with a solid paycheck).\n", "2. Social support (aka having someone who’ll show up when your car breaks down).\n", "3. Healthy life expectancy (you can’t enjoy life if you’re coughing through it).\n", "4. Freedom to make life choices (we all want to be our own boss, maybe just not literally).\n", "5. Generosity (kindness counts, even if it’s just sharing your fries).\n", "6. Perceptions of corruption (no one’s smiling if the system feels rigged).\n", " \n", "But hear me out here’s the twist: the rankings aren’t just cold, hard math (as it may seem). They’re based on how people feel. Which means that even countries with similar circumstances might see the world and their happiness through entirely different lenses." ] }, { "cell_type": "markdown", "id": "086dcbb3-3915-4f52-a9c1-3cd6e717bb99", "metadata": {}, "source": [ "## Let’s Get Visual\n", "\n", "(Don't worry readers whether you know coding or not. We will take this slow and just look at some plots made from this data and see what we find interesting along the way.)\n", "\n", "To begin our analysis, we first gathered the necessary data from the official World Happiness Report website. The report provides valuable insights into the factors that contribute to the happiness of people in various countries. For this analysis, we utilized the Excel file from the official source: World Happiness Report 2024." ] }, { "cell_type": "code", "execution_count": 2, "id": "89b5d028-3333-41e3-9d83-6f91bf0952a4", "metadata": {}, "outputs": [], "source": [ "#Loaded necessary libraries\n", "library(readxl)\n", "library(kableExtra)\n", "library(knitr)\n", "library(DT)\n", "library(tidyr)\n", "library(plotly)\n", "library(dplyr)\n", "library(ggplot2)\n", "\n", "#Downloading and cleaning of the data\n", "url <- \"https://happiness-report.s3.amazonaws.com/2024/DataForFigure2.1+with+sub+bars+2024.xls\"\n", "download.file(url, destfile = \"DataForFigure2.1_with_sub_bars_2024.xls\", mode = \"wb\")\n", "\n", "happy_data <- read_excel(\"DataForFigure2.1_with_sub_bars_2024.xls\") %>%\n", " select(`Country name`, `Ladder score`, `Explained by: Log GDP per capita`, \n", " `Explained by: Social support`, `Explained by: Healthy life expectancy`, \n", " `Explained by: Freedom to make life choices`, `Explained by: Generosity`, \n", " `Explained by: Perceptions of corruption`) %>%\n", " filter(!is.na(`Ladder score`))" ] }, { "cell_type": "markdown", "id": "f0cfcc7c-ae7c-4116-a53d-017140a86a9f", "metadata": {}, "source": [ "To dive deeper, we have whipped up some fancy (but still fun) data visualizations. First, we made an interactive table where you can scroll through each country’s Ladder Score. (Spoiler alert!!!!!: Finland is #1, while Afghanistan holds the last spot). If you’ve ever wondered how these rankings stack up, this table makes it clear." ] }, { "cell_type": "code", "execution_count": 3, "id": "31f65576-d771-4319-9ed4-211769d3e53e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t
\n", "\t\t\n", "\t\t\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\t\n", "\t\n", "\t\t\n", "\n", "\t\n", "\n" ], "text/plain": [ "HTML widgets cannot be represented in plain text (need html)" ] }, "metadata": { "text/html": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "#Interactive table with each countries \"Ladder scores\"\n", "datatable(\n", " happy_data %>% select(`Country name`, `Ladder score`), \n", " options = list(pageLength = 10, autoWidth = TRUE),\n", " caption = \"Countries and their Ladder Score\"\n", ")\n" ] }, { "cell_type": "markdown", "id": "c1f598f1-c8ca-4bed-b788-e51bf64d3d83", "metadata": {}, "source": [ "Let’s just get it out of the way, Finland is the happiest country in the world. Again. Yes, the land of Moomins and saunas is living its best life at the top of the ladder. But who is right behind them? Give us some drumrolls.....It's Denmark then Iceland, and then finally (as we all have waited for rigth?) Sweden, sitting pretty in fourth place. (Go us! Though seriously, are we that happy, or are we just grateful for fika breaks?)\n", "\n", "But wait, what’s this? Israel is right behind us. Israel? With the ongoing conflict with Palestine? It makes you stop and think: \"what’s pushing their score so high?\". Is it the GDP per capita? The social support? A secret stash of serotonin we don’t know about?\n", "\n", "Well let’s dig even deeper into our analysis by examining the contributing factors for each country. We'll plot how each factor (e.g., GDP, social support) contributes to the overall happiness score for each country. And again this is interactive so that you readers can choose to look at the country you find interesting. Fun right, how much you can do with just a simple excel file with some R coding. But enough about that and let us analys what this tells us! 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Score", "updatemenus": [ { "buttons": [ { "args": [ { "text": [ [ "Finland", "Finland", "Finland", "Finland", "Finland", "Finland", "Denmark", "Denmark", "Denmark", "Denmark", "Denmark", "Denmark", "Iceland", "Iceland", "Iceland", "Iceland", "Iceland", "Iceland", "Sweden", "Sweden", "Sweden", "Sweden", "Sweden", "Sweden", "Israel", "Israel", "Israel", "Israel", "Israel", "Israel", "Netherlands", "Netherlands", "Netherlands", "Netherlands", "Netherlands", "Netherlands", "Norway", "Norway", "Norway", "Norway", "Norway", "Norway", "Luxembourg", "Luxembourg", "Luxembourg", "Luxembourg", "Luxembourg", "Luxembourg", "Switzerland", "Switzerland", "Switzerland", "Switzerland", "Switzerland", "Switzerland", "Australia", "Australia", "Australia", "Australia", "Australia", "Australia", "New Zealand", "New Zealand", "New Zealand", "New Zealand", "New Zealand", "New Zealand", "Costa Rica", "Costa Rica", "Costa Rica", "Costa Rica", "Costa Rica", "Costa Rica", "Kuwait", "Kuwait", "Kuwait", "Kuwait", 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"type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.0801, 0.5776, 0.2876, 0.547, 0.1205, 0.1641 ] ] } ], "label": "Ivory Coast", "method": "restyle" }, { "args": [ { "text": [ [ "Guinea", "Guinea", "Guinea", "Guinea", "Guinea", "Guinea" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.8306, 0.6216, 0.2355, 0.5213, 0.2101, 0.1074 ] ] } ], "label": "Guinea", "method": "restyle" }, { "args": [ { "text": [ [ "Turkiye", "Turkiye", "Turkiye", "Turkiye", "Turkiye", "Turkiye" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.7016, 1.175, 0.6311, 0.2021, 0.0681, 0.1148 ] ] } ], "label": "Turkiye", "method": "restyle" }, { "args": [ { "text": [ [ "Senegal", "Senegal", "Senegal", "Senegal", "Senegal", "Senegal" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.9274, 0.7507, 0.3921, 0.6068, 0.152, 0.0687 ] ] } ], "label": "Senegal", "method": "restyle" }, { "args": [ { "text": [ [ "Iran", "Iran", "Iran", "Iran", "Iran", "Iran" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.4352, 1.1362, 0.5708, 0.3655, 0.235, 0.1234 ] ] } ], "label": "Iran", "method": "restyle" }, { "args": [ { "text": [ [ "Azerbaijan", "Azerbaijan", "Azerbaijan", "Azerbaijan", "Azerbaijan", "Azerbaijan" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.4329, 0.8757, 0.4956, 0.6682, 0.1116, 0.1992 ] ] } ], "label": "Azerbaijan", "method": "restyle" }, { "args": [ { "text": [ [ "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nigeria" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.0418, 1.0755, 0.2556, 0.5658, 0.2014, 0.019 ] ] } ], "label": "Nigeria", "method": "restyle" }, { "args": [ { "text": [ [ "Cameroon", "Cameroon", "Cameroon", "Cameroon", "Cameroon", "Cameroon" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.9432, 0.856, 0.2876, 0.5212, 0.1263, 0.0603 ] ] } ], "label": "Cameroon", "method": "restyle" }, { "args": [ { "text": [ [ "Ukraine", "Ukraine", "Ukraine", "Ukraine", "Ukraine", "Ukraine" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.3499, 1.3153, 0.5133, 0.6306, 0.2854, 0.0254 ] ] } ], "label": "Ukraine", "method": "restyle" }, { "args": [ { "text": [ [ "Namibia", "Namibia", "Namibia", "Namibia", "Namibia", "Namibia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.2663, 1.212, 0.3069, 0.4697, 0.0687, 0.0615 ] ] } ], "label": "Namibia", "method": "restyle" }, { "args": [ { "text": [ [ "Morocco", "Morocco", "Morocco", "Morocco", "Morocco", "Morocco" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.2134, 0.4711, 0.4946, 0.6311, 0.0421, 0.0818 ] ] } ], "label": "Morocco", "method": "restyle" }, { "args": [ { "text": [ [ "Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan", "Pakistan" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.0685, 0.6004, 0.3207, 0.5419, 0.1444, 0.074 ] ] } ], "label": "Pakistan", "method": "restyle" }, { "args": [ { "text": [ [ "Niger", "Niger", "Niger", "Niger", "Niger", "Niger" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.5728, 0.6774, 0.2931, 0.6145, 0.145, 0.1468 ] ] } ], "label": "Niger", "method": "restyle" }, { "args": [ { "text": [ [ "Burkina Faso", "Burkina Faso", "Burkina Faso", "Burkina Faso", "Burkina Faso", "Burkina Faso" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7558, 0.6851, 0.2737, 0.4829, 0.1732, 0.1787 ] ] } ], "label": "Burkina Faso", "method": "restyle" }, { "args": [ { "text": [ [ "Mauritania", "Mauritania", "Mauritania", "Mauritania", "Mauritania", "Mauritania" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.0777, 0.7046, 0.4001, 0.3426, 0.1325, 0.1985 ] ] } ], "label": "Mauritania", "method": "restyle" }, { "args": [ { "text": [ [ "Gambia", "Gambia", "Gambia", "Gambia", "Gambia", "Gambia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7503, 0.6838, 0.3297, 0.4586, 0.3243, 0.0478 ] ] } ], "label": "Gambia", "method": "restyle" }, { "args": [ { "text": [ [ "Chad", "Chad", "Chad", "Chad", "Chad", "Chad" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6033, 0.8054, 0.1985, 0.4111, 0.2185, 0.1127 ] ] } ], "label": "Chad", "method": "restyle" }, { "args": [ { "text": [ [ "Kenya", "Kenya", "Kenya", "Kenya", "Kenya", "Kenya" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.0367, 0.8954, 0.3533, 0.5186, 0.2815, 0.0686 ] ] } ], "label": "Kenya", "method": "restyle" }, { "args": [ { "text": [ [ "Tunisia", "Tunisia", "Tunisia", "Tunisia", "Tunisia", "Tunisia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.3064, 0.9547, 0.5791, 0.2539, 0.0239, 0.0184 ] ] } ], "label": "Tunisia", "method": "restyle" }, { "args": [ { "text": [ [ "Benin", "Benin", "Benin", "Benin", "Benin", "Benin" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.9136, 0.1283, 0.284, 0.5668, 0.1121, 0.2521 ] ] } ], "label": "Benin", "method": "restyle" }, { "args": [ { "text": [ [ "Uganda", "Uganda", "Uganda", "Uganda", "Uganda", "Uganda" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.772, 1.1508, 0.3734, 0.5874, 0.1778, 0.0542 ] ] } ], "label": "Uganda", "method": "restyle" }, { "args": [ { "text": [ [ "Myanmar", "Myanmar", "Myanmar", "Myanmar", "Myanmar", "Myanmar" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.9776, 0.9881, 0.4357, 0.4504, 0.4008, 0.1741 ] ] } ], "label": "Myanmar", "method": "restyle" }, { "args": [ { "text": [ [ "Cambodia", "Cambodia", "Cambodia", "Cambodia", "Cambodia", "Cambodia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.011, 1.0185, 0.442, 0.8633, 0.1699, 0.071 ] ] } ], "label": "Cambodia", "method": "restyle" }, { "args": [ { "text": [ [ "Ghana", "Ghana", "Ghana", "Ghana", "Ghana", "Ghana" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.0765, 0.7471, 0.3595, 0.6232, 0.1827, 0.0277 ] ] } ], "label": "Ghana", "method": "restyle" }, { "args": [ { "text": [ [ "Liberia", "Liberia", "Liberia", "Liberia", "Liberia", "Liberia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6188, 0.6733, 0.3007, 0.5458, 0.1778, 0.0745 ] ] } ], "label": "Liberia", "method": "restyle" }, { "args": [ { "text": [ [ "Mali", "Mali", "Mali", "Mali", "Mali", "Mali" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7474, 0.6879, 0.2675, 0.5857, 0.1204, 0.0896 ] ] } ], "label": "Mali", "method": "restyle" }, { "args": [ { "text": [ [ "Madagascar", "Madagascar", "Madagascar", "Madagascar", "Madagascar", "Madagascar" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6279, 0.8235, 0.3332, 0.2505, 0.1724, 0.1232 ] ] } ], "label": "Madagascar", "method": "restyle" }, { "args": [ { "text": [ [ "Togo", "Togo", "Togo", "Togo", "Togo", "Togo" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7585, 0.5861, 0.3201, 0.4526, 0.1267, 0.156 ] ] } ], "label": "Togo", "method": "restyle" }, { "args": [ { "text": [ [ "Jordan", "Jordan", "Jordan", "Jordan", "Jordan", "Jordan" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.2623, 0.983, 0.5943, 0.5932, 0.0593, 0.1895 ] ] } ], "label": "Jordan", "method": "restyle" }, { "args": [ { "text": [ [ "India", "India", "India", "India", "India", "India" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.1662, 0.6527, 0.417, 0.7666, 0.1739, 0.122 ] ] } ], "label": "India", "method": "restyle" }, { "args": [ { "text": [ [ "Egypt", "Egypt", "Egypt", "Egypt", "Egypt", "Egypt" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.37, 0.9959, 0.4877, 0.4897, 0.0254, 0.2592 ] ] } ], "label": "Egypt", "method": "restyle" }, { "args": [ { "text": [ [ "Sri Lanka", "Sri Lanka", "Sri Lanka", "Sri Lanka", "Sri Lanka", "Sri Lanka" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.361, 1.1787, 0.586, 0.5831, 0.1441, 0.0308 ] ] } ], "label": "Sri Lanka", "method": "restyle" }, { "args": [ { "text": [ [ "Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh", "Bangladesh" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.1216, 0.2492, 0.5133, 0.7749, 0.1404, 0.1674 ] ] } ], "label": "Bangladesh", "method": "restyle" }, { "args": [ { "text": [ [ "Ethiopia", "Ethiopia", "Ethiopia", "Ethiopia", "Ethiopia", "Ethiopia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7916, 0.9149, 0.4198, 0.4409, 0.2703, 0.1007 ] ] } ], "label": "Ethiopia", "method": "restyle" }, { "args": [ { "text": [ [ "Tanzania", "Tanzania", "Tanzania", "Tanzania", "Tanzania", "Tanzania" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.8201, 0.706, 0.3797, 0.7093, 0.1912, 0.2572 ] ] } ], "label": "Tanzania", "method": "restyle" }, { "args": [ { "text": [ [ "Comoros", "Comoros", "Comoros", "Comoros", "Comoros", "Comoros" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.8955, 0.328, 0.3702, 0.1721, 0.1285, 0.1597 ] ] } ], "label": "Comoros", "method": "restyle" }, { "args": [ { "text": [ [ "Yemen", "Yemen", "Yemen", "Yemen", "Yemen", "Yemen" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6713, 1.2815, 0.2927, 0.3624, 0.0801, 0.1132 ] ] } ], "label": "Yemen", "method": "restyle" }, { "args": [ { "text": [ [ "Zambia", "Zambia", "Zambia", "Zambia", "Zambia", "Zambia" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.899, 0.8089, 0.264, 0.7271, 0.1676, 0.1093 ] ] } ], "label": "Zambia", "method": "restyle" }, { "args": [ { "text": [ [ "Eswatini", "Eswatini", "Eswatini", "Eswatini", "Eswatini", "Eswatini" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.2553, 0.9251, 0.1759, 0.2841, 0.0594, 0.1159 ] ] } ], "label": "Eswatini", "method": "restyle" }, { "args": [ { "text": [ [ "Malawi", "Malawi", "Malawi", "Malawi", "Malawi", "Malawi" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6169, 0.4104, 0.3491, 0.5709, 0.1348, 0.1358 ] ] } ], "label": "Malawi", "method": "restyle" }, { "args": [ { "text": [ [ "Botswana", "Botswana", "Botswana", "Botswana", "Botswana", "Botswana" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.4447, 0.9686, 0.2414, 0.5667, 0.0144, 0.0821 ] ] } ], "label": "Botswana", "method": "restyle" }, { "args": [ { "text": [ [ "Zimbabwe", "Zimbabwe", "Zimbabwe", "Zimbabwe", "Zimbabwe", "Zimbabwe" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7483, 0.8501, 0.2321, 0.4869, 0.0961, 0.1306 ] ] } ], "label": "Zimbabwe", "method": "restyle" }, { "args": [ { "text": [ [ "Congo (Kinshasa)", "Congo (Kinshasa)", "Congo (Kinshasa)", "Congo (Kinshasa)", "Congo (Kinshasa)", "Congo (Kinshasa)" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.5337, 0.6647, 0.2615, 0.4727, 0.1887, 0.0722 ] ] } ], "label": "Congo (Kinshasa)", "method": "restyle" }, { "args": [ { "text": [ [ "Sierra Leone", "Sierra Leone", "Sierra Leone", "Sierra Leone", "Sierra Leone", "Sierra Leone" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6543, 0.566, 0.2533, 0.469, 0.1814, 0.0533 ] ] } ], "label": "Sierra Leone", "method": "restyle" }, { "args": [ { "text": [ [ "Lesotho", "Lesotho", "Lesotho", "Lesotho", "Lesotho", "Lesotho" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.7708, 0.8506, 0, 0.5232, 0.0821, 0.085 ] ] } ], "label": "Lesotho", "method": "restyle" }, { "args": [ { "text": [ [ "Lebanon", "Lebanon", "Lebanon", "Lebanon", "Lebanon", "Lebanon" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 1.3767, 0.5767, 0.5562, 0.1732, 0.0676, 0.0288 ] ] } ], "label": "Lebanon", "method": "restyle" }, { "args": [ { "text": [ [ "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan" ] ], "type": "bar", "x": [ [ "Log GDP per capita", "Social support", "Healthy life expectancy", "Freedom to make life choices", "Generosity", "Perceptions of corruption" ] ], "y": [ [ 0.6281, 0, 0.2418, 0, 0.0907, 0.0885 ] ] } ], "label": "Afghanistan", "method": "restyle" } ], "type": "dropdown" } ], "xaxis": { "automargin": true, "domain": [ 0, 1 ], "title": "Factors" }, "yaxis": { "automargin": true, "domain": [ 0, 1 ], "title": "Contribution to Ladder Score" } }, "shinyEvents": [ "plotly_hover", "plotly_click", "plotly_selected", "plotly_relayout", "plotly_brushed", "plotly_brushing", "plotly_clickannotation", "plotly_doubleclick", "plotly_deselect", "plotly_afterplot", "plotly_sunburstclick" ], "source": "A", "visdat": { "49544b6a7e82": [ "function () ", "plotlyVisDat" ] } }, "text/html": [ "\n", "\n", "\t\n", "\t\t\n", "\t\t\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\t\n", "\t\n", "\t\t\n", "\n", "\t\n", "\n" ], "text/plain": [ "HTML widgets cannot be represented in plain text (need html)" ] }, "metadata": { "text/html": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "happy_data_long <- happy_data %>%\n", " select(`Country name`, `Ladder score`, \n", " `Explained by: Log GDP per capita`, \n", " `Explained by: Social support`, \n", " `Explained by: Healthy life expectancy`, \n", " `Explained by: Freedom to make life choices`, \n", " `Explained by: Generosity`, \n", " `Explained by: Perceptions of corruption`) %>%\n", " pivot_longer(\n", " cols = starts_with(\"Explained by:\"),\n", " names_to = \"Factor\",\n", " values_to = \"Contribution\"\n", " ) %>%\n", " mutate(Factor = gsub(\"Explained by: \", \"\", Factor)) %>% \n", " filter(!is.na(Contribution)) \n", "#Take away NA values\n", "\n", "#Basdiagram\n", "plot <- happy_data_long %>%\n", " plot_ly(\n", " type = \"bar\"\n", " )\n", "\n", "#Adding every country as a button in the dropdown menu\n", "dropdown_buttons <- lapply(unique(happy_data_long$`Country name`), function(country) {\n", " list(\n", " method = \"restyle\",\n", " args = list(\n", " list(\n", " x = list(happy_data_long$Factor[happy_data_long$`Country name` == country]),\n", " y = list(happy_data_long$Contribution[happy_data_long$`Country name` == country]),\n", " text = list(happy_data_long$`Country name`[happy_data_long$`Country name` == country]),\n", " type = \"bar\"\n", " )\n", " ),\n", " label = country\n", " )\n", "})\n", "\n", "#Add a button to show all the countries \n", "dropdown_buttons <- c(\n", " list(\n", " list(\n", " method = \"restyle\",\n", " args = list(\n", " list(\n", " x = list(happy_data_long$Factor),\n", " y = list(happy_data_long$Contribution),\n", " text = list(happy_data_long$`Country name`),\n", " type = \"bar\"\n", " )\n", " ),\n", " label = \"All Countries\"\n", " )\n", " ),\n", " dropdown_buttons\n", ")\n", "\n", "#Adding a dropdown-menu in the layout\n", "plot <- plot %>%\n", " layout(\n", " title = \"Contribution of Factors to Ladder Score\",\n", " xaxis = list(title = \"Factors\"),\n", " yaxis = list(title = \"Contribution to Ladder Score\"),\n", " updatemenus = list(\n", " list(\n", " type = \"dropdown\",\n", " buttons = dropdown_buttons\n", " )\n", " )\n", " )\n", "\n", "plot" ] }, { "cell_type": "markdown", "id": "188d6e2b-0c6f-4fe0-b770-4dacba2aa5f8", "metadata": {}, "source": [ "Are we really surprised?, GDP per capita seems to be the biggest contributing factor to every countries ladder score. After that comes the score for social support. If you scroll through these countries and look at the factors you can see that many countries haven't given themlselves a high score for perception of corruption or freedom to make life choices. It seems like we all feel bad about being controlled and not having our voices heard. Maybe that is why the score of generosity is as low as it is.\"Treat others the way they treat you!\"(But if they’re a raccoon with a garbage can, then remember not to take your frustration out on the innocent squirrels around you!)\n", "\n", "Let's focus again. To make it easier to compare the contributing factors to the ladder score we now provide a comparison of the top three countries of the ladder score and the bottom three. These countries are as we have already have seen before, if you remember: (we can take it easy we don't need the drumrolls again) Finland, Denmark and Iceland for the top three and Lesotho (never heard of that before), Lebanon and Afghanistan at the bottom. To make it really easy we have a plot that shows each of these countries scores of the factors beside eachother. (We don't have time for more)" ] }, { "cell_type": "code", "execution_count": 8, "id": "6c21ae6e-7d21-463d-9bd2-0a346c3b3db7", "metadata": {}, "outputs": [ { "data": { "image/png": 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CDSgWBopyiSUy1TX7Uh0oFIF0mabBhA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\ndoDRJ9ofd5PkfOBOgj+BZn/tuCyimICmI8ob0IpVBHlYdo3Ufdz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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "#Comparison of top 3 and bottom 3 countries\n", "top_3 <- happy_data %>%\n", " arrange(desc(`Ladder score`)) %>%\n", " head(3)\n", "\n", "bottom_3 <- happy_data %>%\n", " arrange(`Ladder score`) %>%\n", " head(3)\n", "\n", "compare_data <- bind_rows(top_3, bottom_3) %>%\n", " pivot_longer(cols = starts_with(\"Explained by\"), names_to = \"Factor\", values_to = \"Value\")\n", "\n", "ggplot(compare_data, aes(x = `Country name`, y = Value, fill = Factor)) +\n", " geom_bar(stat = \"identity\", position = \"dodge\") +\n", " facet_wrap(~Factor, scales = \"free_y\") +\n", " labs(title = \"Comparison of Top 3 and Bottom 3 Countries by Key Factors\", \n", " y = \"Value\", x = \"Country\") +\n", " theme_minimal() +\n", " theme(axis.text.x = element_text(angle = 45, hjust = 1))\n" ] }, { "cell_type": "markdown", "id": "9c81b7fd-3bd8-4736-aaf9-06236c4ad379", "metadata": {}, "source": [ "Well is there anything that surprises you? The top three countries seems to have high scores on everything right? The one thing that seems to stick out for them is how Finland aren't as generous as Denmark and Iceland (Winners are never generous right!). But they still have higher scores than the bottom three countries. But as we can see the bottom three countries all have low scores for perceptions of corruption and freedom to make life choices (well Lesotho seems to have given them a quite good score for that though, but they have no score for healthy life expectancy), maybe these could explain why they can't be generous. It feels like alot of these score are linked together. \n", "\n", "Even though we may have gathered some more understanding of the data from this analysis we all want to understand how the whole world takes a part into this right. So to get a even better understanding of this world happiness report we have created a world map that shows us the score for each country by using a happiness score bar that shows what the colors in the map represents." ] }, { "cell_type": "code", "execution_count": 6, "id": "e7accb7c-9de5-4275-9b4f-b0677e8a165b", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "attrs": { "49546bef5420": { "alpha_stroke": 1, "colorbar": { "title": "Happiness Score" }, "colorscale": "Viridis", "locationmode": "country names", "locations": {}, "reversescale": false, "sizes": [ 10, 100 ], "spans": [ 1, 20 ], "text": {}, "type": "choropleth", "z": {} } }, "base_url": "https://plot.ly", "config": { "modeBarButtonsToAdd": [ "hoverclosest", "hovercompare" ], "showSendToCloud": false }, "cur_data": "49546bef5420", "data": [ { "colorbar": { "len": 0.5, "lenmode": "fraction", "ticklen": 2, "title": "Happiness Score", "y": 1, "yanchor": "top" }, "colorscale": "Viridis", "locationmode": "country names", "locations": [ "Finland", "Denmark", "Iceland", "Sweden", "Israel", "Netherlands", "Norway", "Luxembourg", "Switzerland", "Australia", "New Zealand", "Costa Rica", "Kuwait", "Austria", "Canada", "Belgium", "Ireland", "Czechia", "Lithuania", "United Kingdom", "Slovenia", "United Arab Emirates", "United States", "Germany", "Mexico", "Uruguay", "France", "Saudi Arabia", "Kosovo", "Singapore", "Taiwan Province of China", "Romania", "El Salvador", "Estonia", "Poland", "Spain", "Serbia", "Chile", "Panama", "Malta", "Italy", "Guatemala", "Nicaragua", "Brazil", "Slovakia", "Latvia", "Uzbekistan", "Argentina", "Kazakhstan", "Cyprus", "Japan", "South Korea", "Philippines", "Vietnam", "Portugal", "Hungary", "Paraguay", "Thailand", "Malaysia", "China", "Honduras", "Bahrain", "Croatia", "Greece", "Bosnia and Herzegovina", "Libya", "Jamaica", "Peru", "Dominican Republic", "Mauritius", "Moldova", "Russia", "Bolivia", "Ecuador", "Kyrgyzstan", "Montenegro", "Mongolia", "Colombia", "Venezuela", "Indonesia", "Bulgaria", "Armenia", "South Africa", "North Macedonia", "Algeria", "Hong Kong S.A.R. of China", "Albania", "Tajikistan", "Congo (Brazzaville)", "Mozambique", "Georgia", "Iraq", "Nepal", "Laos", "Gabon", "Ivory Coast", "Guinea", "Turkiye", "Senegal", "Iran", "Azerbaijan", "Nigeria", "State of Palestine", "Cameroon", "Ukraine", "Namibia", "Morocco", "Pakistan", "Niger", "Burkina Faso", "Mauritania", "Gambia", "Chad", "Kenya", "Tunisia", "Benin", "Uganda", "Myanmar", "Cambodia", "Ghana", "Liberia", "Mali", "Madagascar", "Togo", "Jordan", "India", "Egypt", "Sri Lanka", "Bangladesh", "Ethiopia", "Tanzania", "Comoros", "Yemen", "Zambia", "Eswatini", "Malawi", "Botswana", "Zimbabwe", "Congo (Kinshasa)", "Sierra Leone", "Lesotho", "Lebanon", "Afghanistan" ], "marker": { "line": { "color": "rgba(31,119,180,1)" } }, "reversescale": false, "showscale": true, "text": [ "Country: Finland